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Leukocyte Recognition Using EM-Algorithm

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MICAI 2009: Advances in Artificial Intelligence (MICAI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5845))

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Abstract

This document describes a method for classifying images of blood cells. Three different classes of cells are used: Band Neutrophils, Eosinophils and Lymphocytes. The image pattern is projected down to a lower dimensional sub space using PCA; the probability density function for each class is modeled with a Gaussian mixture using the EM-Algorithm. A new cell image is classified using the maximum a posteriori decision rule.

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References

  1. Hoffbrand, A.V., Pettit, J.E.: Essential Haematology., 3rd edn. Blackwell Science, Malden (1993)

    Google Scholar 

  2. Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, Heidelberg (2006)

    Google Scholar 

  3. Davies, E.R.: Machine Vision: Theory, Algorithms, Practicalities. Morgan Kaufmann Publishers Inc., San Francisco (2004)

    Google Scholar 

  4. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  5. Fluxion Biosciences: Microfluidic flow cytometer with stop-flow capability provides enhanced cellular imaging. Technical report, Fluxion Biosciences, QB3- Mission Bay; 1700 4th St., Suite 219; San Francisco, CA 94143 (2005)

    Google Scholar 

  6. Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall Professional Technical Reference, Englewood Cliffs (2002)

    Google Scholar 

  7. Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. Johns Hopkins University Press (October 11, 1996)

    Google Scholar 

  8. Moghaddam, B., Pentland, A.: Probabilistic visual learning for object detection. In: ICCV 1995, vol. 1, pp. 786–793 (1995)

    Google Scholar 

  9. Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. In: PAMI, vol. 19, pp. 696–710 (July 1997)

    Google Scholar 

  10. Theera-Umpon, N.: Patch-Based White Blood Cell Nucleus Segmentation Using Fuzzy Clustering. Transactions on Electrical Eng., Electronics, and Communications, ECTI-EEC 3, 5–19 (2005)

    Google Scholar 

  11. Sabino, D.M.U., da Fontoura Costa, L., Rizzatti, E.G., Zago, M.A.: A texture approach to leukocyte recognition. Real-Time Imaging 10, 205–216 (2004)

    Article  Google Scholar 

  12. Sanei, S., Lee, T.M.K.: Cell Recognition Based on PCA and Bayesian Classification. In: Fourth International Symposium on Independent Component Analysis and Blind Signal Separation. ICA, April 1 (2003)

    Google Scholar 

  13. Su, M.C., Chou, C.H.: A modified version of the k-means algorithm with a distance based on cluster symmetry. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 674–680 (2001)

    Article  Google Scholar 

  14. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces, pp. 586–591 (1991)

    Google Scholar 

  15. Woermann, U., Montandon, M., Tobler, A., Solemthaler, M.: HemoSurf - An Interactive Hematology Atlas. Division of Instructional Media, Bern (2004)

    Google Scholar 

  16. Webb, A.R.: Statistical pattern recognition. John Wiley & Sons, Chichester (1999)

    MATH  Google Scholar 

  17. Yampri, P., Pintavirooj, C., Daochai, S., Teartulakarn, S.: White Blood Cell Classification based on the Combination of Eigen Cell and Parametric Feature Detection. In: IEEE (ed.) Industrial Electronics and Applications (May 24, 2006)

    Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Colunga, M.C., Siordia, O.S., Maybank, S.J. (2009). Leukocyte Recognition Using EM-Algorithm. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds) MICAI 2009: Advances in Artificial Intelligence. MICAI 2009. Lecture Notes in Computer Science(), vol 5845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05258-3_48

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  • DOI: https://doi.org/10.1007/978-3-642-05258-3_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05257-6

  • Online ISBN: 978-3-642-05258-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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